Increasing product complexity, shorter development cycles and cross-domain integration demands pose significant challenges for modern race car engineering teams. In Formula Student teams, heterogeneous toolchains, manual data exchange, late system integration, and high personnel turnover hinder efficient collaborative development and lead to repeated knowledge loss. This paper presents an integrated digital-engineering framework combining graph-based design languages (GBDL), model-to-text transformations, natural-language interactions via Large Language Models (LLMs), and Git-based version control to address these issues. By formalizing design knowledge and storing it in a centralized design graph, the framework ensures digital consistency of data and models, supports automated vehicle design variant generation, and enables seamless cross-domain integration. Through case studies of three Formula Student teams, the methodology demonstrates quantifiable reductions in design iteration time, enabling the evaluation of more than 10⁴ suspension variants within days instead of a few dozen manually created variants, while reducing hands-on engineering effort from minutes per variant to a largely unattended optimization process. The results indicate that the approach not only enhances efficiency and collaboration but also preserves design knowledge for long-term knowledge management and reuse. Looking forward, this methodology provides a scalable route toward further engineering automation, systematic variant-driven development, and early-stage design optimization supported by design languages and integrated downstream toolchains.

The development of modern vehicles faces an increase in complexity, as well as a need for shorter development cycles and a seamless cross-domain integration. In order to meet these challenges, a graph-based design language which formalizes and automates engineering workflows is presented and applied in a design case study to a Formula Student race car suspension system. The proposed method uses an ontology-based vocabulary definition and executable model transformations to compile design knowledge into a central and consistent design graph. This graph enables the automatic generation of consistent 3D CAD models, domain-specific simulations and suspension kinematic analyses, replacing manual and error-prone tool and data handover processes. The design language captures both the structural and dynamic behavior of the suspension, supports variant exploration and allows for integrated validation, such as 3D collision detection. The study illustrates how graph-based design languages can serve as ‘digital DNA’ for knowledge-based product development, offering a scalable, reusable platform for engineering automation. This approach enhances the digital consistency of data, the digital continuity of processes and the digital interoperability of tools across all relevant engineering disciplines in order to support the validation of early-stage designs and the optimization of complex systems.

Current research is investigating strategies, methods, and tools aimed at making vehicles more fault-tolerant and resilient. However, one level of product concretization—the level of abstract physics—was not yet in the focus of the scientific discourse. As a consequence of an increasing complexity of technical systems, the approaches fault-tolerant design and resilient design become even more important and decisive product characteristics concern the level of abstract physics. The paper analyses opportunities and approaches on this level, which are intended to allow an accommodation of faults and/or to increase the resilience of vehicle systems. Four different areas of potential improvement were identified. A systematic procedure and a conscious integration in industrial vehicle development processes were investigated. The research results were developed in several current vehicle component development processes and are explained using this basis. The presented research intends to open a new field of investigation, consequently a main emphasis is directed to future research contents and directions.

The engineering design of complex systems increasingly relies on model-based methods that generate large-scale models with a vast multitude of couplings between the functional requirements and design parameters. The so-called Axiomatic Design Approach tries to characterize and evaluate the couplings between functional requirements ($FR$s) and design parameters ($DP$s) by imposing a square matrix form for the mapping. In full generality however, the physical modeling of complex systems produces rectangular matrices due to an a priori unequal number of $FR$s and $DP$s, as dictated by physical laws and already shown with mathematical rigour in previous works. By using the full rectangular form of the coupling matrix, this paper presents an analytical framework that uses classical sensitivity-based analysis to identify, quantify, and visualize coupling structures represented by rectangular design matrices. Normalized visualizations of the Jacobian matrix in the form of so-called heatmaps then help to reveal intrinsic subsystem structures and highlight areas of strong internal couplings and minimal inter-subsystem interactions. The heatmap thus helps to detect weakly coupled subsystem blocks, supports a more systematic decomposition and allows a targeted optimization in a loose analogy to the original idea of the so-called Independence Axiom in axiomatic design. This methodology permits the sequential or even independent optimization of subsystems or parts thereof, facilitating a reliable and efficient system-level design. The proposed approach is demonstrated through two applications: a Formula Student race car and a small satellite design. The case studies illustrate how rectangular matrix analysis can provide a practical basis for guiding system decomposition and design decisions in model-based engineering systems.